K-Nearest Neighbor

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What Does K-Nearest Neighbor Mean?

A k-nearest-neighbor algorithm, often abbreviated k-nn, is an approach to data classification that estimates how likely a data point is to be a member of one group or the other depending on what group the data points nearest to it are in.

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The k-nearest-neighbor is an example of a “lazy learner” algorithm, meaning that it does not build a model using the training set until a query of the data set is performed.

Techopedia Explains K-Nearest Neighbor

A k-nearest-neighbor is a data classification algorithm that attempts to determine what group a data point is in by looking at the data points around it.

An algorithm, looking at one point on a grid, trying to determine if a point is in group A or B, looks at the states of the points that are near it. The range is arbitrarily determined, but the point is to take a sample of the data. If the majority of the points are in group A, then it is likely that the data point in question will be A rather than B, and vice versa.

The k-nearest-neighbor is an example of a “lazy learner” algorithm because it does not generate a model of the data set beforehand. The only calculations it makes are when it is asked to poll the data point’s neighbors. This makes k-nn very easy to implement for data mining.

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Margaret Rouse
Editor

Margaret jest nagradzaną technical writerką, nauczycielką i wykładowczynią. Jest znana z tego, że potrafi w prostych słowach pzybliżyć złożone pojęcia techniczne słuchaczom ze świata biznesu. Od dwudziestu lat jej definicje pojęć z dziedziny IT są publikowane przez Que w encyklopedii terminów technologicznych, a także cytowane w artykułach ukazujących się w New York Times, w magazynie Time, USA Today, ZDNet, a także w magazynach PC i Discovery. Margaret dołączyła do zespołu Techopedii w roku 2011. Margaret lubi pomagać znaleźć wspólny język specjalistom ze świata biznesu i IT. W swojej pracy, jak sama mówi, buduje mosty między tymi dwiema domenami, w ten…